A gabor based technique for image denoising
Why this work is in the frame
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Bibliographic record
Abstract
As an alternative to the wavelet, Gabor function has been used as an efficient representation of two dimensional signals. We are interested in BayesShrink techniques for image denoising, and have shown in our previous work that BayesShrink Ridgelet performs better than VisuShrink ridgelet and VisuShrink wavelet. In this paper, a dyadic Gabor filter bank is combined with BayesShrink method for image denoising. In the proposed method, the noisy image is decomposed to different channels in several levels by a dyadic Gabor filter bank. To recover the image, the corrupting noise is removed by applying the proposed BayesShrink method on the noisy Gabor coefficients. The noise variance is estimated in Gabor domain and the estimated noise is then used to dynamically calculate an individual threshold for each spatio-frequency channel. Finally denoised coefficients are transformed back to reconstruct the image.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it